Effect of synbiotic supplementation in children and adolescents with cystic fibrosis: a randomized controlled clinical trial

BACKGROUND/OBJECTIVES:Cystic fibrosis (CF) is characterized by excessive activation of immune processes. The aim of this study was to evaluate the effect of synbiotic supplementation on the inflammatory response in children/adolescents with CF. SUBJECTS/METHODS:A randomized, placebo-controlled, double-blind, clinical-trial was conducted with control group (CG, n = 17), placebo-CF-group (PCFG, n = 19), synbiotic CF-group (SCFG, n = 22), PCFG negative (n = 8) and positive (n = 11) bacteriology, and SCFG negative (n = 12) and positive (n = 10) bacteriology. Markers of lung function (FEV1), nutritional status [body mass index-for age (BMI/A), height-for-age (H/A), weight-for-age (W/A), upper-arm fat area (UFA), upper-arm muscle area (UMA), body fat (%BF)], and inflammation [interleukin (IL)-12, tumor necrosis factor-alpha (TNF-α), IL-10, IL-6, IL-1β, IL-8, myeloperoxidase (MPO), nitric oxide metabolites (NOx)] were evaluated before and after 90-day of supplementation with a synbiotic. RESULTS:No significance difference was found between the baseline and end evaluations of FEV1 and nutricional status markers. A significant interaction (time vs. group) was found for IL-12 (p = 0.010) and myeloperoxidase (p = 0.036) between PCFG and SCFG, however, the difference was not maintained after assessing the groups individually. NOx diminished significantly after supplementation in the SCFG (p = 0.030). In the SCFG with positive bacteriology, reductions were found in IL-6 (p = 0.033) and IL-8 (p = 0.009) after supplementation. CONCLUSIONS: Synbiotic supplementation shown promise at diminishing the pro-inflammatory markers IL-6, IL-8 in the SCFG with positive bacteriology and NOx in the SCFG in children/adolescents with CF

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Public intervention in food and nutrition in Brazil

En las últimas dos décadas, el gobierno brasileño creó varios programas de transferencia de ingresos para los más pobres con el objetivo de promover la seguridad alimentaria y nutricional, así como para erradicar la pobreza extrema y el hambre. Estos programas proporcionaron algunos resultados satisfactorios, lo que no se puede atribuir exclusivamente a la transferencia de ingresos, sino también a otros sectores gubernamentales y a diversas políticas públicas en las áreas de educación, salud y saneamiento básico. En conjunto, estas políticas están destinadas a romper el patrón de pobreza intergeneracional, contribuyendo con el desarrollo humano del país